The mechanical characteristics (e.g., strength, formability, and tenacity), electromagnetic characteristics (e.g., magnetic permeability), and other properties of metallic materials inclusive of ferroalloys and aluminum alloys vary not only with the chemical composition of the particular alloy, but also with its heating conditions, its processing conditions, and its cooling conditions. The composition of an alloy is conditioned by controlling an adding rate of constituent element(s). The lot sizes of products during quality governing, however, are too great to change an actual adding rate for each product. To manufacture products of desired quality, therefore, it is very important to enhance product quality by establishing appropriate heating, processing, and cooling conditions.
A typical traditional control method has been by determining independent data based on many years of experience, such as a heating temperature target value, after-processing dimensional target value, and cooling rate target value, for heating, processing, and cooling conditions each, and for each set of product specifications, and then conducting temperature control and dimensional control to attain the above target data. In recent years, however, the significantly growing sophisticatedness and diversity of the product specifications called for have caused a case in which the desired materials quality cannot be obtained because of appropriate target data not always being determined using such an experiential method.
In recent years is therefore known a control method in which a materials quality model for estimating product quality from heating conditions, processing conditions, and cooling conditions, is used to determine these conditions for each process through computations to obtain the product quality matching to target data. Patent Reference 1, for example, describes such a control method.
Another known method is by sampling measured plate thickness and materials temperature data during rolling and then using these data samplings as input data for a materials quality model in order to improve accuracy. In this method, before the rolling of a steel material is started, the materials quality model is used to determine the heating conditions, rolling conditions, and cooling conditions of the steel material from its composition data, its after-rolling size, and its guaranteed quality data. In addition, when measured plate thickness, material temperature, interpass time, roll diameter, and roll speed data is obtained following completion of a heating process, a pre-rolling process, and a finish-rolling process, a schedule concerning the next and subsequent rolling or cooling process conditions, based on the measured data, is set up using the materials quality model to suppress variations in product quality. Patent Reference 2, for example, describes such a control method.
Meanwhile, a control method that uses a neural network in lieu of a materials quality model is known. This method is used to examine the characteristics of processed or heat-treated metallic materials and assign examination results as teaching data to a neural network to improve the accuracy of prediction with the neural network. Patent Reference 3, for example, describes such a control method.    [Patent Reference 1] Japanese Patent Publication No. 7-102378    [Patent Reference 2] Japanese Patent No. 2509481    [Patent Reference 3] Japanese Patent Laid-open No. 2001-349883